Navigation apparatus and operation method of navigation apparatus

ABSTRACT

An operation method of a navigation apparatus includes: obtaining valid global positioning system (GPS) data at a current time point corresponding to a current position of a target device; determining first neighboring map elements corresponding to a first region indicated by the valid GPS data at the current time point from among a plurality of map elements of map data; and determining a pose parameter of the target device at the current time point based on a first direction specified by at least a portion of the first neighboring map elements.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/848,330 filed on Apr. 14, 2020, which claims the benefit under 35 USC119(a) of Korean Patent Application No. 10-2019-0144100 filed on Nov.12, 2019, in the Korean Intellectual Property Office, the entiredisclosure of which is incorporated herein by reference for allpurposes.

BACKGROUND 1. Field

The following description relates to a navigation apparatus and anoperation method of a navigation apparatus.

2. Description of Related Art

An existing navigation apparatus may provide information associated witha direction in which a vehicle travels or proceeds and with a positionof the vehicle by receiving information associated with a positionthrough a global positioning system (GPS) and matching the receivedinformation to a two-dimensional (2D) map. For matching the receivedinformation to the 2D map, there are methods of calculating a distancebetween the position of the vehicle obtained through the GPS and aposition in a road link, and matching the position of the vehicle to aposition of the vehicle in the road link having the shortest distancefrom the position of the vehicle, or methods of estimating a directionbased on road geometry information, road connecting point information,and a rotation angle of each node and matching the estimated directionto the map. A level of accuracy of such an existing navigation apparatusmay depend on such matching technology, and thus a high level ofaccuracy may be related to accurate matching.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

In one general aspect, an operation method of a navigation apparatusincludes: obtaining valid global positioning system (GPS) data at acurrent time point corresponding to a current position of a targetdevice; determining first neighboring map elements corresponding to afirst region indicated by the valid GPS data at the current time pointfrom among a plurality of map elements of map data; and determining apose parameter of the target device at the current time point based on afirst direction specified by at least a portion of the first neighboringmap elements.

The first direction may be a three-dimensional (3D) direction.

The pose parameter may include a roll parameter, a pitch parameter, anda yaw parameter.

The determining of the pose parameter at the current time point based onthe first direction may include identifying the first direction byperforming line fitting with a plurality of points included in the firstneighboring map elements and respectively corresponding to 3D positions.

The determining of the pose parameter at the current time point based onthe first direction may include: determining a GPS-based yaw based onthe valid GPS data at the current time point and a position of thetarget device at a previous time point; and extracting a sample from thefirst neighboring map elements by comparing a map-based yawcorresponding to each of the first neighboring map elements to thedetermined GPS-based yaw.

The determining of the pose parameter at the current time point based onthe first direction may further include identifying the first directionby applying a random sample consensus (RANSAC) algorithm to theextracted sample.

The determining of the pose parameter at the current time point based onthe first direction may include determining the pose parameter bycomparing a sensor-based yaw at the current time point and a map-basedyaw corresponding to the first direction. The sensor-based yaw at thecurrent time point may be calculated by applying a yaw rate measuredthrough a steering sensor of the target device to a yaw of the targetdevice at a previous time point.

The pose parameter may be determined to correspond to the firstdirection, in response to a difference between the sensor-based yaw andthe map-based yaw being less than a threshold value.

The operation method may further include: determining a direction cosinematrix (DCM) corresponding to the determined pose parameter of thetarget device at the current time point; and determining a velocityparameter of the target device at the current time point by applying theDCM to a velocity vector corresponding to a velocity of the targetdevice at the current time point that is measured through a velocitysensor of the target device.

The operation method of may further include: determining a map-basedlane by matching the valid GPS data at the current time to the map data;determining a sensor-based lane by applying a lane change trigger to asensor-based position of the target device at the current time pointthat is calculated by applying dead reckoning (DR) to a position of thetarget device at a previous time point; and determining a positionparameter of the target device at the current time point by comparingthe map-based lane and the sensor-based lane.

The operation may further include: generating the lane change trigger bycomparing a lane width and a change in a position of the target devicein a lateral direction.

The operation method may further include determining whether the validGPS data is obtained.

The operation method of claim 12, wherein the determining of whether thevalid GPS data is obtained includes: obtaining GPS data at the currenttime point corresponding to the current position of the target device;determining a GPS-based velocity and a GPS-based yaw rate based on theobtained GPS data at the current time point; obtaining a sensor-basedvelocity measured through a velocity sensor of the target device and asensor-based yaw rate measured through a steering sensor of the targetdevice; and determining validity of the GPS data based on a result ofcomparing the GPS-based velocity and the sensor-based velocity with aresult of comparing the GPS-based yaw rate and the sensor-based yawrate.

In another general aspect, a non-transitory computer-readable storagemedium stores instructions that, when executed by a processor, cause theprocessor to perform the operation described above.

In another general aspect, an operation method of a navigation apparatusincludes: obtaining a sensor-based position at a current time point thatis calculated by applying dead reckoning (DR) to a previous position ofa target device and corresponds to a current position of the targetdevice, in response to valid global positioning system (GPS) data at thecurrent time point corresponding to the current position of the targetdevice not being obtained; determining neighboring map elementscorresponding to a region indicated by the sensor-based position at thecurrent timepoint from among a plurality of map elements; anddetermining a pose parameter of the target device at the current timepoint based on a direction specified by at least a portion of theneighboring map elements.

The determining of the pose parameter at the current time point based onthe direction may include identifying the direction by performing linefitting with a plurality of points that are included in the neighboringmap elements and respectively correspond to 3D positions.

The determining of the pose parameter at the current time point based onthe direction may include identifying the direction by applying a RANSACalgorithm to the neighboring map elements.

The determining of the pose parameter at the current time point based onthe direction may include: determining a pitch and a roll in the poseparameter based on the direction; and determining a yaw in the poseparameter by applying a yaw rate measured through a steering sensor ofthe target device to a yaw of the target device at a previous timepoint.

In another general aspect, a navigation apparatus includes a processorconfigured to: obtain valid global positioning system (GPS) data at acurrent time point corresponding to a current position of a targetdevice; determine first neighboring map elements corresponding to afirst region indicated by the valid GPS data at the current time pointfrom among a plurality of map elements of map data; and determine a poseparameter of the target device at the current time point based on afirst direction specified by at least a portion of the first neighboringmap elements.

The processor may be further configured to: identify the first directionby performing line fitting with a plurality of points that are includedin the first neighboring map elements and respectively correspond tothree-dimensional (3D) positions.

The processor may be further configured to: determine a GPS-based yawbased on the valid GPS data at the current time point and a position ofthe target device at a previous time point; and extract a sample fromthe first neighboring map elements by comparing a map-based yawcorresponding to each of the first neighboring map elements to thedetermined GPS-based yaw.

The processor may be further configured to identify the first directionby applying a random sample consensus (RANSAC) algorithm to theextracted sample.

The processor may be further configured to determine the pose parameterby comparing a sensor-based yaw at the current time point and amap-based yaw corresponding to the first direction. The sensor-based yawat the current time point may be calculated by applying a yaw ratemeasured through a steering sensor of the target device to a yaw of thetarget device at a previous time point.

The precision navigation apparatus of claim 23, wherein the poseparameter is determined to correspond to the first direction, inresponse to a difference between the sensor-based yaw and the map-basedyaw being less than a threshold value.

The processor may be further configured to, in response to the valid GPSdata at the current time point not being obtained: obtain a sensor-basedposition at the current time point that is calculated by applying deadreckoning (DR) to a previous position of the target device andcorresponds to the current position of the target device; determinesecond neighboring map elements corresponding to a second regionindicated by the obtained sensor-based position at the current timepoint from among the plurality of map elements; and determine the poseparameter of the target device at the current time point based on asecond direction specified by at least a portion of the secondneighboring map elements.

The processor may be further configured to: determine a direction cosinematrix (DCM) corresponding to the determined pose parameter of thetarget device at the current time point; and determine a velocityparameter of the target device at the current time point by applying theDCM to a velocity vector corresponding to a velocity of the targetdevice at the current time point that is measured through a velocitysensor of the target device.

The processor may be further configured to: determine a map-based laneby matching the valid GPS data at the current time point to the mapdata; determine a sensor-based lane by applying a lane change trigger toa sensor-based position of the target device at the current time pointthat is calculated by applying dead reckoning (DR) to a position of thetarget device at a previous time point; and determine a positionparameter of the target device at the current time point by comparingthe map-based lane and the sensor-based lane.

The processor may be further configured to generate the lane changetrigger by comparing a change in a position of the target in a lateraldirection, and a lane width.

The navigation apparatus may further include: a memory storinginstructions, wherein the processor is configured to execute theinstructions to perform the obtaining of the valid global positioningsystem (GPS) data, the determining of the first neighboring mapelements, and the determining of the pose parameter.

Other features and aspects will be apparent from the following detaileddescription, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating an example of an input and an output ofa navigation apparatus.

FIG. 2 is a diagram illustrating an example of a pose parameter.

FIG. 3 is a diagram illustrating an example of an operation of anavigation apparatus.

FIG. 4 is a diagram illustrating an example of determining validity ofglobal positioning system (GPS) data.

FIG. 5 is a diagram illustrating an example of map data.

FIG. 6 is a diagram illustrating an example of determining a poseparameter.

FIG. 7 is a diagram illustrating an example of determining a velocityparameter.

FIG. 8 is a diagram illustrating an example of determining a positionparameter.

FIG. 9 is a diagram illustrating an example of determining a lanechange.

FIG. 10 is a flowchart illustrating an example of an operation method ofa navigation apparatus.

FIG. 11 is a diagram illustrating an example of a configuration of anavigation apparatus.

Throughout the drawings and the detailed description, the same referencenumerals refer to the same elements, features, and structures. Thedrawings may not be to scale, and the relative size, proportions, anddepiction of elements in the drawings may be exaggerated for clarity,illustration, and convenience.

DETAILED DESCRIPTION

The following detailed description is provided to assist the reader ingaining a comprehensive understanding of the methods, apparatuses,and/or systems described herein. However, various changes,modifications, and equivalents of the methods, apparatuses, and/orsystems described herein will be apparent after an understanding of thedisclosure of this application. For example, the sequences of operationsdescribed herein are merely examples, and are not limited to those setforth herein, but may be changed as will be apparent after anunderstanding of the disclosure of this application, with the exceptionof operations necessarily occurring in a certain order. Also,descriptions of features that are known after an understanding of thedisclosure of this application may be omitted for increased clarity andconciseness.

The features described herein may be embodied in different forms and arenot to be construed as being limited to the examples described herein.Rather, the examples described herein have been provided merely toillustrate some of the many possible ways of implementing the methods,apparatuses, and/or systems described herein that will be apparent afteran understanding of the disclosure of this application.

Herein, it is noted that use of the term “may” with respect to anexample or embodiment, e.g., as to what an example or embodiment mayinclude or implement, means that at least one example or embodimentexists in which such a feature is included or implemented while allexamples and embodiments are not limited thereto.

Throughout the specification, when an element, such as a layer, region,or substrate, is described as being “on,” “connected to,” or “coupledto” another element, it may be directly “on,” “connected to,” or“coupled to” the other element, or there may be one or more otherelements intervening therebetween. In contrast, when an element isdescribed as being “directly on,” “directly connected to,” or “directlycoupled to” another element, there can be no other elements interveningtherebetween. As used herein, the term “and/or” includes any one and anycombination of any two or more of the associated listed items.

Although terms such as “first,” “second,” and “third” may be used hereinto describe various members, components, regions, layers, or sections,these members, components, regions, layers, or sections are not to belimited by these terms. Rather, these terms are only used to distinguishone member, component, region, layer, or section from another member,component, region, layer, or section. Thus, a first member, component,region, layer, or section referred to in examples described herein mayalso be referred to as a second member, component, region, layer, orsection without departing from the teachings of the examples.

The terminology used herein is for describing various examples only andis not to be used to limit the disclosure. The articles “a,” “an,” and“the” are intended to include the plural forms as well, unless thecontext clearly indicates otherwise. The terms “comprises,” “includes,”and “has” specify the presence of stated features, numbers, operations,members, elements, and/or combinations thereof, but do not preclude thepresence or addition of one or more other features, numbers, operations,members, elements, and/or combinations thereof.

Unless otherwise defined, all terms, including technical and scientificterms, used herein have the same meaning as commonly understood by oneof ordinary skill in the art to which this disclosure pertains and basedon an understanding of the disclosure of this application. Terms, suchas those defined in commonly used dictionaries, are to be interpreted ashaving a meaning that is consistent with their meaning in the context ofthe relevant art and the disclosure of this application and are not tobe interpreted in an idealized or overly formal sense unless expresslyso defined herein.

The features of the examples described herein may be combined in variousways as will be apparent after an understanding of the disclosure ofthis application. Further, although the examples described herein have avariety of configurations, other configurations are possible as will beapparent after an understanding of the disclosure of this application.

Also, in the description of example embodiments, detailed description ofstructures or functions that are thereby known after an understanding ofthis application may be omitted.

FIG. 1 is a diagram illustrating an example of an input and an output ofa precision navigation apparatus.

Referring to FIG. 1 , a precision navigation apparatus 100 (hereinafter,“navigation apparatus 100”) may provide precise information associatedwith a physical state of a target device. For example, the navigationapparatus 100 may provide a navigation parameter indicating any one orany combination of any two or more of a pose, a velocity, and a positionbased on any one or any combination of any two or more of velocity data,steering data, global positioning system (GPS) data, and map data. Inthis example, the steering data may include steering angle data.

The target device may include various devices that may need precisestate information. For example, the target device may be one of varioustypes of augmented reality (AR) devices including, for example, anaugmented reality head-up display (AR HUD) device, a vehicle includingan AR HUD, a mobile device providing AR, and the like. The AR device maydisplay a real background overlaid with a virtual image based on a stateof the AR device. To embody an AR environment without an error, the ARdevice may need to accurately measure the state of the AR. For anotherexample, the target device may be an autonomous vehicle that needsprecise positioning.

The navigation parameter may be applied in various fields ofapplications. For example, the navigation parameter may be used toprovide a user with navigation information, provide an autonomousvehicle with travel control information, and the like. In an example,the navigation parameter may be used in an initial term of sensorfusion. The sensor fusion is a method of combining a plurality ofvarious types of sensors into one and providing a solution. For example,the sensor fusion may be used for determining a pose, a velocity, and aposition of a vehicle.

When an output of the sensor fusion converges on a true value, thesensor fusion may produce a relatively accurate output. However, wheninitial information with a relatively great error is provided to thesensor fusion, a relatively long period of time may be needed for anoutput of the sensor fusion to converge on a true value. For example, insuch a case, an AR device may fail to match a virtual image to a realbackground for such a long period of time. The navigation apparatus 100may generate a relatively accurate navigation parameter, and thegenerated accurate navigation parameter may be used in the initial termof the sensor fusion to improve performance of the sensor fusion interms of accuracy. For example, the navigation parameter of thenavigation apparatus 100 may be used as initial information for thesensor fusion, and then provided for the sensor fusion for a period oftime until an output of the sensor fusion converges a the true value.

As described above, the navigation apparatus 100 may use velocity data,steering data, GPS data, and map data to generate a navigationparameter. For example, the map data used herein may be based on ahigh-definition (HD) map. The HD map may include information associatedwith various elements, for example, lanes, centerlines, and trafficsigns or markings, that are generated based on various sensors. Thesevarious elements of the HD map may be represented by point cloud, andeach point in the point cloud may correspond to a three-dimensional (3D)position. The navigation apparatus 100 may generate a precise navigationparameter including a 3D pose, a 3D velocity, and a lane-level positionusing such an HD map.

In addition, the velocity data, the steering data, and the GPS data thatare used to generate the navigation parameter may be obtainedrespectively through a velocity sensor, a steering sensor, and a GPSreceiver that are generally used in a vehicle or a mobile device. Thevelocity sensor may include an odometer, for example. The steeringsensor may include a steering wheel and a gyroscope, for example. Thus,implementing the precision navigation system 100 may not need someadditional and expensive sensors such as a light detection and ranging(LiDAR) sensor, for example.

FIG. 2 is a diagram illustrating an example of a pose parameter.Referring to FIG. 2 , illustrated is an x-y-z 3D coordinate space. Apose parameter may include a roll parameter, a pitch parameter, and ayaw parameter. The roll parameter may indicate an inclination withrespect to an x axis. The pitch parameter may indicate an inclinationwith respect to a y axis. The yaw parameter may indicate an inclinationwith respect to a z axis. The x axis may correspond to a direction inwhich a target device travels or proceeds, for example.

A pose of the target device may be indicated by a line corresponding toa 3D direction. In such a case, the roll parameter may correspond to avalue of c in a two-dimensional (2D) line, for example, z=cy, that isobtained by projecting the corresponding 3D line to a zy plane. Thepitch parameter may correspond to a value of b in a 2D line, forexample, z=bx, that is obtained by projecting the corresponding 3D lineto an xz plane. The yaw parameter may correspond to a value of a in a 2Dline, for example, y=ax, that is obtained by projecting thecorresponding 3D line to an xy plane.

Hereinafter, the roll parameter, the pitch parameter, and the yawparameter will be indicated by φ, θ, and ψ, respectively. In addition, ayaw may also be referred to as a heading.

FIG. 3 is a diagram illustrating an example of an operation of anavigation apparatus. Referring to FIG. 3 , in operation 310, anavigation apparatus determines GPS validity. Based on a result ofoperation 310, it may be determined whether valid GPS data is obtained.

A circumstance in which the valid GPS data is obtained may include acase in which GPS data is received and the received GPS data is valid. Acircumstance in which the valid GPS data is not obtained may include acase in which GPS data is received and the received GPS data is notvalid, or a case in which GPS data is not received. GPS data may bereceived periodically based on a preset reception period, for example, 1second. The case in which the GPS data is not received may include acase in which GPS data is not received due to a communication failurewhen the reception period elapses, and a case in which GPS data is notreceived between reception periods. For example, the case in which GPSdata is not received between reception periods may be a case in whichother data is needed to replace GPS data between GPS reception periods,as in a case in which a frame rate of an AR image exceeds 1 frame persecond (fps).

The navigation apparatus may determine the validity of the GPS data bycomparing a GPS-based velocity and a GPS-based yaw rate to asensor-based velocity and a sensor-based yaw rate, respectively. TheGPS-based velocity and the GPS-based yaw rate are a velocity and a yawrate, respectively, that are measured using the GPS data. Thesensor-based velocity and the sensor-based yaw rate are a velocity and ayaw rate, respectively, that are measured using a sensor, for example, avelocity sensor and a steering sensor.

For example, the navigation apparatus may obtain GPS data at a currenttimepoint corresponding to a current position of a target device, anddetermine a GPS-based velocity and a GPS-based yaw rate based on theobtained GPS data at the current time point. In addition, the precisionnavigation apparatus may obtain a sensor-based velocity measured througha velocity sensor of the target device, and a sensor-based yaw ratemeasured through a steering sensor of the target device. The navigationapparatus may then determine validity of the GPS data based on a resultof comparing the GPS-based velocity and the sensor-based velocity and aresult of comparing the GPS-based yaw rate and the sensor-based yawrate. When a difference between the GPS-based velocity and thesensor-based velocity and a difference between the GPS-based yaw rateand the sensor-based yaw rate are all less than respective thresholdvalues, the navigation apparatus may determine that the GPS data isvalid. Hereinafter, an example of determining validity of GPS data willbe described in detail with reference to FIG. 4 .

FIG. 4 is a diagram illustrating an example of determining validity ofGPS data. A navigation apparatus may determine validity of GPS data byreferring to a highly robust sensor, for example, a velocity sensor anda steering sensor. Referring to FIG. 4 , in operation 410, thenavigation apparatus may determine a GPS-based velocity V_(GPS) and aGPS-based yaw ψ_(GPS) based on a position P_(GPS,t) corresponding to GPSdata at a current time point. For example, the GPS-based velocityV_(GPS) and the GPS-based yaw ψ_(GPS) may be determined as representedby Equations 1 and 2.

$\begin{matrix}{V_{GPS} = \frac{P_{{GPS},t} - P_{{GPS},{t - 1}}}{\Delta t}} & \left\lbrack {{Equation}1} \right\rbrack\end{matrix}$ $\begin{matrix}{\psi_{GPS} = {\tan^{- 1}\frac{V_{{GPS},{lon}}}{V_{{GPS},{lat}}}}} & \left\lbrack {{Equation}2} \right\rbrack\end{matrix}$

In Equation 1, P_(GPS,t−1) is a position corresponding to GPS data at aprevious time point t−1. Δt is a difference between a current time pointt and the previous time point t−1, and corresponds to a processingperiod of the navigation apparatus. For example, the navigationapparatus may generate a navigation parameter each Δt.

In this example, GPS data may include information associated with alatitude, a longitude, and an altitude, and thus a positioncorresponding to the GPS data may be a 3D position. In Equation 2,V_(GPS,lon) is a GPS-based measured velocity in a longitudinaldirection, and V_(GPS,lat) is a GPS-based measured velocity in alatitudinal direction. Thus, the velocities V_(GPS,lon) and V_(GPS,lat)may respectively correspond to a longitudinal component and alatitudinal component of a velocity V_(GPS).

In operation 420, the navigation apparatus may determine a velocitydifference diff_(vel) and an angle difference diff_(ang). The velocitydifference diff_(vel) and the angle difference diff_(ang) may bedetermined as represented by Equations 3 and 4.

diff_(vel) =|∥V _(GPS) ∥−V _(car)|  [Equation 3]

diff_(ang)=|{dot over (ψ)}_(GPS)−{dot over (ψ)}_(car)|  [Equation 4]

In Equation 3, ∥V_(GPS)∥ is a magnitude of V_(GPS), and V_(car) is avelocity of a target device that is measured through a velocity sensorof the target device. In Equation 4, {dot over (ψ)}_(GPS) is a GPS-basedyaw rate, and {dot over (ψ)}_(car) is a sensor-based yaw rate. TheGPS-based yaw rate may be determined based on a GPS-based yaw at aprevious time point and a GPS-based yaw at a current time point. Thesensor-based yaw rate may be measured through a steering sensor of thetarget device.

In operation 430, the navigation apparatus may compare a velocitydifference diff_(vel) and an angle difference diff_(ang) with respectivethreshold values thres_(v) and thres_(a).

When the velocity difference diff_(vel) is less than the threshold valuethres_(v) and the angle difference diff_(ang) is less than the thresholdvalue thres_(a), the navigation apparatus may determine that the GPSdata at the current time point is valid. In contrast, when the velocitydifference diff_(vel) is greater than the threshold value thres_(v) andthe angle difference diff_(ang) is greater than the threshold valuethres_(a), the navigation apparatus may determine that the GPS data atthe current time point is not valid.

Referring back to FIG. 3 , in operation 320, the navigation apparatusmay determine a pose parameter. For example, the navigation apparatusobtains valid GPS data at a current time point corresponding to acurrent position of a target device, and determines first neighboringmap elements corresponding to a first region indicated by the valid GPSdata at the current time point from among a plurality of map elements ofmap data. For example, a circumstance in which the first neighboring mapelements correspond to the first region may also indicate that the firstneighboring map elements are included in the first region. The firstregion may be a region in a range, for example, 10 meters (m), from aposition indicated by the valid GPS data at the current time point.Subsequently, the navigation apparatus may determine the pose parameterof the target device at the current time point based on a firstdirection indicated by at least a portion of the first neighboring mapelements.

As described above, the map data may be based on an HD map. The HD mapmay include various map elements including, for example, lanes,centerlines, and traffic signs and markings. The map elements in the HDmap may be represented by point cloud, and each point of the point cloudmay correspond to a 3D position.

The navigation apparatus may perform line fitting on a plurality ofpoints included in the first neighboring map elements, and identify adirection of each of the first neighboring map elements. As describedabove, each point may correspond to a 3D position, and thus a directionof each of the first neighboring map elements may also correspond to a3D direction. The navigation apparatus may identify the first directionbased on a result of the line fitting.

The navigation apparatus may identify the first direction by selectingat least a portion of the first neighboring map elements from among thefirst neighboring map elements. Through such a selection, map elementsthat are closely and actually associated with the target device may beselected as neighboring map elements. For example, when a vehicle passesthrough an intersection, a map element irrelevant to a travel directionof the vehicle, for example, a lane in a clockwise direction or aright-turn direction, may be selected as a neighboring map element. Sucha neighboring map element may be irrelevant to an actual traveldirection of the vehicle, and may thus have an influence as an error inindicating a pose of the vehicle. Thus, through the selection, such anerror element may be eliminated, and accuracy of the pose parameter maybe improved.

For example, the navigation apparatus may determine a GPS-based yawbased on the valid GPS data at the current time point and a position ofthe target device at a previous time point. In this example, theposition of the target device at the previous time point may be based onvalid GPS data at the previous time point. However, when the valid GPSdata is not present at the previous time point, the position of thetarget device at the previous time point may be calculated by applyingdead reckoning (DR) to valid GPS data at a further previous time point.

The navigation apparatus may then compare a map-based yaw correspondingto each of the first neighboring map elements with the determinedGPS-based yaw, and extract a sample from the first neighboring mapelements. As described above, a 3D direction corresponding to each ofthe first neighboring map elements may be determined as a result of theline fitting performed on the first neighboring map elements. Inaddition, as described above with reference to FIG. 2 , a map-based yawcorresponding to each of the first neighboring map elements may bedetermined by projecting, to an xy plane, a line corresponding to the 3Ddirection. The navigation apparatus may then extract, as a sample, afirst neighboring map element corresponding to a map-based yaw of whicha difference from the GPS-based yaw is less than a threshold value.

In addition, the navigation apparatus may identify the first directionby applying a random sample consensus (RANSAC) algorithm to samplesextracted as described above. Through the RANSAC algorithm, averaging ofsamples having high similarities to each other among the samples may beperformed, and a direction corresponding to a result of the RANSACalgorithm may be identified as the first direction. Thus, a neighboringelement obstructive of pose estimation of the target device among thesamples may be additionally eliminated, and thus the first direction maybe more likely to correspond to an actual pose of the target device.

The navigation apparatus may determine a map-based first yaw parameter,a map-based first pitch parameter, and a map-based first roll parameter,based on the first direction. The navigation apparatus may determineeach of the parameters by projecting a 3D line corresponding to thefirst direction to each 2D plane as described above with reference toFIG. 2 . Alternatively, the navigation apparatus may determine each ofthe parameters by performing line fitting, on each 2D plane, with pointsincluded in the first neighboring map elements corresponding to thefirst direction.

In addition, the navigation apparatus may determine the pose parameterby verifying the map-based first yaw parameter, the map-based firstpitch parameter, and the map-based first roll parameter. For example,the navigation apparatus may determine the pose parameter by comparing asensor-based yaw at a current time point and the map-based first yawcorresponding to the first direction. In this example, the sensor-basedyaw at the current time point may be calculated by applying, to a yaw ofthe target device at a previous timepoint, a yaw rate that is measuredthrough a steering sensor of the target device.

When a difference between the sensor-based yaw and the map-based firstyaw is less than a threshold value, the navigation apparatus maydetermine the pose parameter to correspond to the first direction. Thatis, the navigation apparatus may determine the map-based first yawparameter, the map-based first pitch parameter, and the map-based firstroll parameter to be the pose parameter. When the difference betweensensor-based yaw and the map-based first yaw is greater than thethreshold value, the navigation apparatus may determine a separatelydetermined sensor-based yaw parameter, a map-based second pitchparameter, and a map-based second roll parameter to be the poseparameter. The sensor-based yaw parameter, the map-based second pitchparameter, and the map-based second roll parameter may be used whenvalid GPS data is not obtained, and determining the sensor-based yawparameter, the map-based second pitch parameter, and the map-basedsecond roll parameter will be described in greater detail hereinafter.

When the valid GPS data at the current time point is not obtained, thenavigation apparatus may obtain a sensor-based position at the currenttime point corresponding to a current position of the target device. Thesensor-based position at the current time point may be calculated byapplying DR to a previous position of the target device. For example,the navigation apparatus may calculate the sensor-based position at thecurrent time point by applying, to the previous position of the targetdevice, the DR that is based on velocity data and steering data.

Subsequently, the navigation apparatus may determine second neighboringmap elements corresponding to a second region indicated by thesensor-based position at the current time point from among a pluralityof map elements of map data, and determine the pose parameter of thetarget device at the current time point based on a second directionindicated by at least a portion of the second neighboring map elements.The navigation apparatus may identify the second direction by performingline fitting with a plurality of points included in the secondneighboring map elements.

However, when the valid GPS data is not obtained, a GPS-based yaw maynot be calculated, and thus a RANSAC may be performed without additionalsampling. For example, the navigation apparatus may identify the seconddirection by applying a RANSAC algorithm to the second neighboring mapelements. Subsequently, the navigation apparatus may determine themap-based second pitch parameter and the map-based second roll parameterbased on the second direction. In this example, a yaw parameter may beseparately calculated based on DR. For example, the navigation apparatusmay determine a sensor-based yaw parameter by applying a yaw ratemeasured through the steering sensor of the target device to a yaw ofthe target device at a previous time point. The navigation apparatus maythen determine the sensor-based yaw parameter, the map-based secondpitch parameter, and the map-based second roll parameter to be the poseparameter.

FIG. 5 is a diagram illustrating an example of map data. Referring toFIG. 5 , map data 520 includes various map elements such as, forexample, lanes, centerlines, traffic signs and markings, and the like.The map elements in the map data 520 may be represented by apoint cloud.A GPS-based position P_(GPS) may be specified on the map data 520 basedon GPS data, and a region including the GPS-based position P_(GPS) maybe set to be a first region 510. A map element corresponding to thefirst region 510 may be selected as a first neighboring map element.However, when a valid GPS signal is not received, a sensor-basedposition may be specified on the map data 520, and a second regionincluding the sensor-based position may be set in order to determine asecond neighboring map element.

FIG. 6 is a diagram illustrating an example of determining a poseparameter. Referring to FIG. 6 , operations 611 through 615 to bedescribed hereinafter may correspond to a case in which valid GPS datais obtained, and operations 621 through 623 to be described hereinaftermay correspond to a case in which valid GPS is not obtained. Even insuch a case in which the valid GPD data is obtained, some of operations621 through 623 may also be performed to provide data needed to performoperations 611 through 615.

In operation 611, a navigation apparatus may determine first neighboringmap elements map1 _(i) based on a GPS-based position P_(GPS,t)corresponding to valid GPS data at a current time point, and on mapdata. In operation 612, the navigation apparatus determines a sample Sfrom the first neighboring map elements map1 _(i). The navigationapparatus may determine the sample S as represented by Equation 5.

$\begin{matrix}{S = \begin{Bmatrix}{{map}_{i}❘} \\{{\psi_{{map},i} - \psi_{gps}} < {thres}}\end{Bmatrix}} & \left\lbrack {{Equation}5} \right\rbrack\end{matrix}$

In Equation 5, ψ_(map,i) is a map-based yaw corresponding to each of thefirst neighboring map elements map1 _(i). ψ_(GPS) is a GPS-based yaw,and thres is a threshold value. In addition, i is an identifier toidentify each of the first neighboring map elements map1 _(i). In thisexample, a neighboring map element map, corresponding to a map-based yawψ_(map,l), of which a difference from the GPS-based yaw ψ_(GPS) is lessthan the threshold value, may be determined to be the sample S.

In operation 613, the navigation apparatus may determine a map-basedfirst yaw parameter ψ_(map1,t), a map-based first pitch parameterθ_(map1,t), and a map-based first roll parameter φ_(map1,t) byperforming a RANSAC on the sample S. Hereinafter, the determinedmap-based first yaw parameter ψ_(map1,t), map-based first pitchparameter θ_(map1,t), and map-based first roll parameter φ_(map1,t) willbe referred to as a “first parameter group” for convenience ofdescription.

In operations 614 and 615, the navigation apparatus may verify the firstparameter group. For example, the navigation apparatus calculates anangle difference diff_(ang) between a sensor-based yaw parameterψ_(car,t) and the map-based first yaw parameter ψ_(map1,t) in operation614, and compares the angle difference diff_(ang) with the thresholdvalue thres_(a) in operation 615. The sensor-based yaw ψ_(car,t) may becalculated in operation 623.

When the angle difference diff_(ang) is less than the threshold valuethres_(a), the first parameter group may be determined to be a poseparameter at the current time point. When the angle differencediff_(ang) is greater than the threshold value thres_(a), a secondparameter group may be determined to be the pose parameter at thecurrent time point. In this example, the second parameter group mayinclude the sensor-based yaw parameter ψ_(car,t), a map-based secondpitch parameter θ_(map2,t), and a map-based second roll parameterφ_(map2,t). The map-based second pitch parameter θ_(map2,t) and themap-based second roll parameter φ_(map2,t) may be calculated inoperation 622.

In operation 621, the navigation apparatus may determine secondneighboring map elements map2 _(i) based on a sensor-based positionP_(car,t) at the current time point and the map data. The sensor-basedposition P_(car,t) may be calculated by applying DR that is based onvelocity data and steering data to a previous position of a targetdevice at a previous time point. In operation 622, the navigationapparatus may determine the map-based second pitch parameter θ_(map2,t)and the map-based second roll parameter φ_(map2,t) by performing aRANSAC on the second neighboring map elements map1 ₂.

In operation 623, the navigation apparatus may determine thesensor-based yaw parameter ψ_(car,t) at the current time point byapplying a yaw rate Δψ_(steer) to a yaw parameter ψ_(car,t−1) at theprevious time point. In this example, the yaw rate Δψ_(steer) maycorrespond to a variation Δψ_(steer) in steering data during Δt, and bemeasured through a steering sensor of the target device. When the validGPS data is not obtained, the second parameter group including thesensor-based yaw parameter ψ_(car,t), the map-based second pitchparameter θ_(map2,t), and the map-based roll parameter φ_(map2,t) may bedetermined to be the pose parameter at the current time input.

Referring back to FIG. 3 , the navigation apparatus determines avelocity parameter. The navigation apparatus may determine a velocityparameter at a current time point using a pose parameter at a currenttime point that is determined in operation 320. For example, thenavigation apparatus may determine a direction cosine matrix (DCM)corresponding to the pose parameter at the current time point, anddetermine the velocity parameter at the current time point by applyingthe DCM to a velocity vector corresponding to a velocity of the targetdevice at the current time point that is measured through a velocitysensor of the target device.

FIG. 7 is a diagram illustrating an example of determining a velocityparameter. Referring to FIG. 7 , in operation 710, a navigationapparatus determines a pose parameter including, for example, ψ_(t),θ_(t), and φ_(t). Operation 710 may correspond to operation 320described above with reference to FIG. 3 . In operation 720, thenavigation apparatus may calculate a DCM corresponding to the poseparameter including ψ_(t), θ_(t), and φ_(t). In operation 730, thenavigation apparatus may determine a velocity parameter based onvelocity data and the DCM. In operation 730, the following equation 6may be used.

$\begin{matrix}{\begin{bmatrix}V_{N} \\V_{E} \\V_{D}\end{bmatrix} = {C_{b}^{n}\begin{bmatrix}V \\0 \\0\end{bmatrix}}} & \left\lbrack {{Equation}6} \right\rbrack\end{matrix}$

In Equation 6, V_(N), V_(E), and V_(D) are a velocity in a northwarddirection, a velocity in an eastward direction, and a velocity in adownward direction, respectively. In addition, V is velocity data andC_(b) ^(n) is a DCM. Through Equation 6, a 3D velocity vectorcorresponding to the velocity parameter may be obtained.

In general, GPS data may have relatively inaccurate altitudeinformation, and thus it may not be easy to apply corresponding GPSinformation as a 3D velocity of a vehicle. In addition, even thoughhighly accurate GPS data is received, it may not be easy to obtain the3D velocity due to a minute horizontal error. However, according to anexample embodiment, it may be possible to obtain a relatively accurate3D velocity vector using a highly accurate pose parameter.

Referring back to FIG. 3 , the navigation apparatus determines aposition parameter in operation 340, and determines a lane change inoperation 350. For example, the navigation apparatus may determine amap-based lane by matching, to map data, valid GPS data at a currenttime point, and determine a sensor-based lane by applying a lane changetrigger to a sensor-based position of a target device at the currenttime point. The sensor-based position of the target device at thecurrent time point may be calculated by applying DR to a position of thetarget device at a previous time point. The lane change trigger may begenerated by comparing a positional change in a lateral direction of thetarget device and a lane width, and may have a value corresponding tothe number of changed lanes. Subsequently, the navigation apparatus maycompare the map-based lane with the sensor-based lane, and determine theposition parameter of the target device at the current time point.

FIG. 8 is a diagram illustrating an example of determining a positionparameter. Referring to FIG. 8 , operations 811, 812, and 813 maycorrespond to a case in which valid GPS data is obtained, and operations821, 822, 823, and 824 may correspond to a case in which valid GPS datais not obtained. However, even when the valid GPS data is obtained, someof operations 821 through 824 may be performed to provide necessary datato perform operations 811 through 813.

In operation 811, a navigation apparatus may perform map matching basedon map data and a GPS-based position P_(GPS,t) corresponding valid GPSdata at a current time point. A map-based position P_(map,t) may begenerated as a result of the map matching. The navigation apparatus maymatch, to the map-based position P_(map,t), a center of a lane closestto the GPS-based position P_(GPS,t).

In operation 812, the navigation apparatus may detect a lane based onthe map-based position P_(map,t). In this operation, a map-based lanelane_(map,t) may be generated as a result of such lane detection.Through operation 812, a lane corresponding to a target device may bedetected among various lanes on a road on which the target device islocated. In operation 813, the navigation apparatus may compare themap-based lane lane_(map,t) with a sensor-based lane lane_(car,t). Thesensor-based lane lane_(car,t) may be determined through operation 823.

In response to the map-based lane lane_(map,t) and the sensor-based lanelane_(car,t) corresponding to each other, the map-based positionP_(map,t) may be determined to be a position parameter. In contrast, inresponse to the map-based lane lane_(map,t) and the sensor-based lanelane_(car,t) not corresponding to each other, a sensor-based positionP_(car,t) may be determined to be the position parameter. A lane changetrigger Δlane may be generated based on a sensor, and may thus be usedto provide relatively accurate lane information. Thus, only when themap-based lane lane_(map,t) corresponds to the sensor-based lanelane_(car,t) that is based on the lane change trigger Δlane, themap-based position P_(map,t) may be adopted as the position parameter.

In operation 821, the navigation apparatus may determine a velocityparameter V_(car,t). Operation 821 may correspond to operation 330described above with reference to FIG. 3 . In operation 822, thenavigation apparatus may calculate the sensor-based position P_(car,t)at the current time point by applying DR to a sensor-based positionP_(car,t−1) at a previous time point. Here, the DR may be performedbased on a variation Δψ_(steer) in the velocity parameter V_(car,t)during Δt.

In operation 823, the navigation apparatus may determine the lane-basedlane lane_(car,t) by applying the lane change trigger Δlane to thesensor-based position P_(car,t−1). In operation 824, the navigationapparatus may generate the lane change trigger Δlane by determining alane change. For example, the navigation apparatus may generate the lanechange trigger Δlane by comparing a change in a position of the targetdevice in a lateral direction, and a lane width. When the valid GPS datais not obtained, the sensor-based position P_(car,t) may be determinedto be the position parameter.

FIG. 9 is a diagram illustrating an example of determining a lanechange. Referring to FIG. 9 , in operation 910, a navigation apparatusmay determine a pose parameter. Operation 910 may correspond tooperation 320 described above with reference to FIG. 3 . In operation920, the navigation apparatus may calculate DCM(C_(b) ^(n)) based on asensor-based yaw parameter ψ_(car,t) and a map-based yaw parameterψ_(map,t). DCM(C_(b) ^(n)) may correspond to a difference between thesensor-based yaw parameter ψ_(car,t) and the map-based yaw parameterψ_(map,t). The map-based yaw parameter ψ_(map,t) may correspond toeither one of a map-based first yaw parameter ψ_(map1,t) and a map-basedsecond yaw parameter ψ_(map2,t) that is finally determined to be thepose parameter.

In operation 930, the navigation apparatus may calculate a positionalchange Δx in a longitudinal direction and a positional change Δy in alateral direction based on DCM(C_(b) ^(n)), a sensor-based velocityV_(car), and Δt.

$\begin{matrix}{\begin{bmatrix}{\Delta x} \\{\Delta y}\end{bmatrix} = {\sum{{{C_{b}^{r}\begin{bmatrix}V_{car} \\0\end{bmatrix}} \cdot \Delta}t}}} & \left\lbrack {{Equation}7} \right\rbrack\end{matrix}$

In operation 940, the navigation apparatus may generate a lane changetrigger Δlane by comparing the positional change Δy in the lateraldirection and a lane width. As a travel distance of the target device inthe longitudinal direction is accumulated, the positional change Δy inthe lateral direction may increase. Thus, when a magnitude of thepositional change Δy in the lateral direction exceeds the lane width,the lane change trigger Δlane may be generated. The lane change triggerΔlane may have a value corresponding to the number of changed lanes.

FIG. 10 is a flowchart illustrating an example of an operation method ofa navigation apparatus. Referring to FIG. 10 , in operation 1010, anavigation apparatus may obtain valid GPS data at a current time pointcorresponding to a current position of a target device. In operation1020, the navigation apparatus may determine first neighboring mapelements corresponding to a first region indicated by the valid GPs dataat the current time point from among a plurality of map elements of mapdata. In operation 1030, the navigation apparatus may determine a poseparameter of the target device at the current time point based on afirst direction specified by at least a portion of the first neighboringmap elements. The navigation apparatus may also perform operationsdescribed above with reference to FIGS. 1 through 9 , and a moredetailed and repeated description will be omitted here for brevity.

FIG. 11 is a diagram illustrating an example of a configuration of anavigation apparatus. Referring to FIG. 11 , a navigation apparatus 1100may include a processor 1110 and a memory 1120. The memory 1120 may beconnected to the processor 1110 and configured to store instructions tobe executed by the processor 1110, and data to be processed by theprocessor 1110 and/or data having been processed by the processor 1110.The memory 1120 may include a non-transitory computer-readable storagemedium, for example, a high-speed random-access memory (RAM) and/or anonvolatile computer-readable storage medium (e.g., at least one diskstorage device, flash memory device, or other nonvolatile solid-statememory devices).

The processor 1110 may execute instructions to perform one or more, orall, of operations and methods described above with reference to FIGS. 1through 10 . For example, the processor 1110 may obtain valid GPS dataat a current time point corresponding to a current position of a targetdevice, determine first neighboring map elements corresponding to afirst region indicated by the valid GPS data at the current time pointfrom among a plurality of map elements of map data, and determine a poseparameter of the target device at the current time based on a firstdirection indicated by at least a portion of the first neighboring mapelements. In addition, the navigation apparatus 1100 may perform otheroperations and methods described above with reference to FIGS. 1 through10 , and a more detailed and repeated description of such operations andmethods will be omitted here for brevity.

The navigation apparatuses 100 and 1100, the processor 1110, the memory1120, the other navigation apparatuses, processors, and memories, theother apparatuses, devices, units, modules, and other components inFIGS. 1 to 11 that perform the operations described in this applicationare implemented by hardware components configured to perform theoperations described in this application that are performed by thehardware components. Examples of hardware components that may be used toperform the operations described in this application where appropriateinclude controllers, sensors, generators, drivers, memories,comparators, arithmetic logic units, adders, subtractors, multipliers,dividers, integrators, and any other electronic components configured toperform the operations described in this application. In other examples,one or more of the hardware components that perform the operationsdescribed in this application are implemented by computing hardware, forexample, by one or more processors or computers. A processor or computermay be implemented by one or more processing elements, such as an arrayof logic gates, a controller and an arithmetic logic unit, a digitalsignal processor, a microcomputer, a programmable logic controller, afield-programmable gate array, a programmable logic array, amicroprocessor, or any other device or combination of devices that isconfigured to respond to and execute instructions in a defined manner toachieve a desired result. In one example, a processor or computerincludes, or is connected to, one or more memories storing instructionsor software that are executed by the processor or computer. Hardwarecomponents implemented by a processor or computer may executeinstructions or software, such as an operating system (OS) and one ormore software applications that run on the OS, to perform the operationsdescribed in this application. The hardware components may also access,manipulate, process, create, and store data in response to execution ofthe instructions or software. For simplicity, the singular term“processor” or “computer” may be used in the description of the examplesdescribed in this application, but in other examples multiple processorsor computers may be used, or a processor or computer may includemultiple processing elements, or multiple types of processing elements,or both. For example, a single hardware component or two or morehardware components may be implemented by a single processor, or two ormore processors, or a processor and a controller. One or more hardwarecomponents may be implemented by one or more processors, or a processorand a controller, and one or more other hardware components may beimplemented by one or more other processors, or another processor andanother controller. One or more processors, or a processor and acontroller, may implement a single hardware component, or two or morehardware components. A hardware component may have any one or more ofdifferent processing configurations, examples of which include a singleprocessor, independent processors, parallel processors,single-instruction single-data (SISD) multiprocessing,single-instruction multiple-data (SIMD) multiprocessing,multiple-instruction single-data (MISD) multiprocessing, andmultiple-instruction multiple-data (MIMD) multiprocessing.

The methods illustrated in FIGS. 1-11 that perform the operationsdescribed in this application are performed by computing hardware, forexample, by one or more processors or computers, implemented asdescribed above executing instructions or software to perform theoperations described in this application that are performed by themethods. For example, a single operation or two or more operations maybe performed by a single processor, or two or more processors, or aprocessor and a controller. One or more operations may be performed byone or more processors, or a processor and a controller, and one or moreother operations may be performed by one or more other processors, oranother processor and another controller. One or more processors, or aprocessor and a controller, may perform a single operation, or two ormore operations.

Instructions or software to control computing hardware, for example, oneor more processors or computers, to implement the hardware componentsand perform the methods as described above may be written as computerprograms, code segments, instructions or any combination thereof, forindividually or collectively instructing or configuring the one or moreprocessors or computers to operate as a machine or special-purposecomputer to perform the operations that are performed by the hardwarecomponents and the methods as described above. In one example, theinstructions or software include machine code that is directly executedby the one or more processors or computers, such as machine codeproduced by a compiler. In another example, the instructions or softwareincludes higher-level code that is executed by the one or moreprocessors or computer using an interpreter. The instructions orsoftware may be written using any programming language based on theblock diagrams and the flow charts illustrated in the drawings and thecorresponding descriptions in the specification, which disclosealgorithms for performing the operations that are performed by thehardware components and the methods as described above.

The instructions or software to control computing hardware, for example,one or more processors or computers, to implement the hardwarecomponents and perform the methods as described above, and anyassociated data, data files, and data structures, may be recorded,stored, or fixed in or on one or more non-transitory computer-readablestorage media. Examples of a non-transitory computer-readable storagemedium include read-only memory (ROM), random-access memory (RAM), flashmemory, CD-ROMs, CD-Rs, CD+Rs, CD-RWs, CD+RWs, DVD-ROMs, DVD-Rs, DVD+Rs,DVD-RWs, DVD+RWs, DVD-RAMs, BD-ROMs, BD-Rs, BD-R LTHs, BD-REs, magnetictapes, floppy disks, magneto-optical data storage devices, optical datastorage devices, hard disks, solid-state disks, and any other devicethat is configured to store the instructions or software and anyassociated data, data files, and data structures in a non-transitorymanner and provide the instructions or software and any associated data,data files, and data structures to one or more processors or computersso that the one or more processors or computers can execute theinstructions. In one example, the instructions or software and anyassociated data, data files, and data structures are distributed overnetwork-coupled computer systems so that the instructions and softwareand any associated data, data files, and data structures are stored,accessed, and executed in a distributed fashion by the one or moreprocessors or computers.

While this disclosure includes specific examples, it will be apparentafter an understanding of the disclosure of this application thatvarious changes in form and details may be made in these exampleswithout departing from the spirit and scope of the claims and theirequivalents. The examples described herein are to be considered in adescriptive sense only, and not for purposes of limitation. Descriptionsof features or aspects in each example are to be considered as beingapplicable to similar features or aspects in other examples. Suitableresults may be achieved if the described techniques are performed in adifferent order, and/or if components in a described system,architecture, device, or circuit are combined in a different manner,and/or replaced or supplemented by other components or theirequivalents. Therefore, the scope of the disclosure is defined not bythe detailed description, but by the claims and their equivalents, andall variations within the scope of the claims and their equivalents areto be construed as being included in the disclosure.

What is claimed is:
 1. An operation method of a navigation apparatus,the operation method comprising: obtaining a sensor-based position at acurrent time point that is calculated by applying dead reckoning (DR) toa previous position of a target device and corresponds to a currentposition of the target device, in response to valid global positioningsystem (GPS) data at the current time point corresponding to the currentposition of the target device not being obtained; determiningneighboring map elements corresponding to a region indicated by thesensor-based position at the current timepoint from among a plurality ofmap elements; and determining a pose parameter of the target device atthe current time point based on a direction specified by at least aportion of the neighboring map elements.
 2. The operation method ofclaim 1, wherein the determining of the pose parameter at the currenttime point based on the direction comprises identifying the direction byperforming line fitting with a plurality of points that are included inthe neighboring map elements and respectively correspond to 3Dpositions.
 3. The operation method of claim 1, wherein the determiningof the pose parameter at the current time point based on the directioncomprises identifying the direction by applying a RANSAC algorithm tothe neighboring map elements.
 4. The operation method of claim 1,wherein the determining of the pose parameter at the current time pointbased on the direction comprises: determining a pitch and a roll in thepose parameter based on the direction; and determining a yaw in the poseparameter by applying a yaw rate measured through a steering sensor ofthe target device to a yaw of the target device at a previous timepoint.
 5. The operation method of claim 1, wherein the determining ofthe pose parameter at the current time point based on the directioncomprises determining the pose parameter by comparing a sensor-based yawat the current time point and a map-based yaw corresponding to thedirection, and wherein the sensor-based yaw at the current time point iscalculated by applying a yaw rate measured through a steering sensor ofthe target device to a yaw of the target device at a previous timepoint.
 6. The operation method of claim 5, wherein the pose parameter isdetermined to correspond to the direction, in response to a differencebetween the sensor-based yaw and the map-based yaw being less than athreshold value.
 7. A non-transitory computer-readable storage mediumstoring instructions that, when executed by a processor, cause theprocessor to perform the operation method of claim
 1. 8. A navigationapparatus, comprising: a processor configured to: obtain a sensor-basedposition at a current time point that is calculated by applying deadreckoning (DR) to a previous position of a target device and correspondsto a current position of the target device, in response to valid globalpositioning system (GPS) data at the current time point corresponding tothe current position of the target device not being obtained; determineneighboring map elements corresponding to a region indicated by thesensor-based position at the current timepoint from among a plurality ofmap elements; and determine a pose parameter of the target device at thecurrent time point based on a direction specified by at least a portionof the neighboring map elements.
 9. The apparatus of claim 8, whereinthe processor is further configured to identify the direction byperforming line fitting with a plurality of points that are included inthe neighboring map elements and respectively correspond to 3Dpositions.
 10. The apparatus of claim 8, wherein the processor isfurther configured to identify the direction by applying a RANSACalgorithm to the neighboring map elements.
 11. The apparatus of claim 8,wherein the processor is further configured to: determine a pitch and aroll in the pose parameter based on the direction; and determine a yawin the pose parameter by applying a yaw rate measured through a steeringsensor of the target device to a yaw of the target device at a previoustime point.
 12. The apparatus of claim 8, wherein the processor isfurther configured to determine the pose parameter by comparing asensor-based yaw at the current time point and a map-based yawcorresponding to the direction, and wherein the sensor-based yaw at thecurrent time point is calculated by applying a yaw rate measured througha steering sensor of the target device to a yaw of the target device ata previous time point.
 13. The apparatus of claim 12, wherein the poseparameter is determined to correspond to the direction, in response to adifference between the sensor-based yaw and the map-based yaw being lessthan a threshold value.